Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)

Adsorption process has an edge in wastewater treatment over other techniques due to low initial cost, sludge free, ease of operation and insensitivity to toxic substance. It is a very essential part in the wastewater treatment process chain. It involves both physical and chemical phenomena and he...

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Main Authors:	Muyibi, Suleyman Aremu, Olanrewaju, Rashidah Funke
Format:	Conference or Workshop Item
Language:	English English
Published:	IDOSI Publication 2013
Subjects:	Q Science (General) QA Mathematics
Online Access:	http://irep.iium.edu.my/30641/ http://irep.iium.edu.my/30641/ http://irep.iium.edu.my/30641/1/ICBIoE_ExtendedAbstract_Adsorption.pdf http://irep.iium.edu.my/30641/4/tentative_programme_R_Olewanraju.pdf

id	iium-30641
recordtype	eprints
spelling	iium-306412013-09-20T08:10:40Z http://irep.iium.edu.my/30641/ Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN) Muyibi, Suleyman Aremu Olanrewaju, Rashidah Funke Q Science (General) QA Mathematics Adsorption process has an edge in wastewater treatment over other techniques due to low initial cost, sludge free, ease of operation and insensitivity to toxic substance. It is a very essential part in the wastewater treatment process chain. It involves both physical and chemical phenomena and hence susceptible to high percentage of errors due to human factor, variation in the quality as well as chemical/physical characteristics of raw materials used. In order to reduce this percentage error and obtain optimal treatment efficiency, an intelligent method of predicting optimal adsorption capacity based on Artificial Neural Network (ANN) was proposed. Production of Powdered Activation Carbon PAC from processed oil palm empty fruit bunches, EFB was used as adsorbent. Since production of PAC is affected by many parameters, such as CO2 gas flow rate, activation time and activation temperature. Adsorption design was carried out using all these parameters, production results were analyzed. ANN was used to forecast optimal adsorption capacity for aqueous phenol removal. Such ANN based system will be a useful method to address most errors common in wastewater treatment cause by human factors. Experimental results on real data show that the newly developed system is able to accurately predict the optimal adsorption capacity needed in wastewater treatment plant. The Regression and correlation between of optimal adsorption capacity for experimental result and ANN estimation model is 0.9999 of 1.000. This high Correlation of coefficient indicates that the ANN model is a perfect match. IDOSI Publication 2013 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/30641/1/ICBIoE_ExtendedAbstract_Adsorption.pdf application/pdf en http://irep.iium.edu.my/30641/4/tentative_programme_R_Olewanraju.pdf Muyibi, Suleyman Aremu and Olanrewaju, Rashidah Funke (2013) Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN). In: 3rd International Conference on Biotechnology (ICBioE'13), 2-4 July, 2013, Berjaya Timesquare Hotel, Kuala Lumpur. http://www.iium.edu.my/icbioe/2013/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	Q Science (General) QA Mathematics
spellingShingle	Q Science (General) QA Mathematics Muyibi, Suleyman Aremu Olanrewaju, Rashidah Funke Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
description	Adsorption process has an edge in wastewater treatment over other techniques due to low initial cost, sludge free, ease of operation and insensitivity to toxic substance. It is a very essential part in the wastewater treatment process chain. It involves both physical and chemical phenomena and hence susceptible to high percentage of errors due to human factor, variation in the quality as well as chemical/physical characteristics of raw materials used. In order to reduce this percentage error and obtain optimal treatment efficiency, an intelligent method of predicting optimal adsorption capacity based on Artificial Neural Network (ANN) was proposed. Production of Powdered Activation Carbon PAC from processed oil palm empty fruit bunches, EFB was used as adsorbent. Since production of PAC is affected by many parameters, such as CO2 gas flow rate, activation time and activation temperature. Adsorption design was carried out using all these parameters, production results were analyzed. ANN was used to forecast optimal adsorption capacity for aqueous phenol removal. Such ANN based system will be a useful method to address most errors common in wastewater treatment cause by human factors. Experimental results on real data show that the newly developed system is able to accurately predict the optimal adsorption capacity needed in wastewater treatment plant. The Regression and correlation between of optimal adsorption capacity for experimental result and ANN estimation model is 0.9999 of 1.000. This high Correlation of coefficient indicates that the ANN model is a perfect match.
format	Conference or Workshop Item
author	Muyibi, Suleyman Aremu Olanrewaju, Rashidah Funke
author_facet	Muyibi, Suleyman Aremu Olanrewaju, Rashidah Funke
author_sort	Muyibi, Suleyman Aremu
title	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_short	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_full	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_fullStr	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_full_unstemmed	Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)
title_sort	prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using artificial neural network (ann)
publisher	IDOSI Publication
publishDate	2013
url	http://irep.iium.edu.my/30641/ http://irep.iium.edu.my/30641/ http://irep.iium.edu.my/30641/1/ICBIoE_ExtendedAbstract_Adsorption.pdf http://irep.iium.edu.my/30641/4/tentative_programme_R_Olewanraju.pdf
first_indexed	2023-09-18T20:44:51Z
last_indexed	2023-09-18T20:44:51Z
_version_	1777409606874562560

Prediction of optimal adsorption of aqueous phenol removal with oil palm empty fruit bunch activated carbon using Artificial Neural Network (ANN)

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